import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__a    :	str         =    logging.get_logger(__name__)

__a    :	List[str]         =    {"""vocab_file""": """spiece.model"""}

__a    :	Any         =    {
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

__a    :	List[Any]         =    {
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
__a    :	Any         =    0
__a    :	List[Any]         =    1
__a    :	str         =    2
__a    :	Union[str, Any]         =    3
__a    :	int         =    4




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					int     					=		VOCAB_FILES_NAMES
			__a    :					Optional[Any]     					=		PRETRAINED_VOCAB_FILES_MAP
			__a    :					Optional[Any]     					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__a    :					str     					=		'''left'''





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=False ,  lowerCAmelCase__=True ,  lowerCAmelCase__=False ,  lowerCAmelCase__="<s>" ,  lowerCAmelCase__="</s>" ,  lowerCAmelCase__="<unk>" ,  lowerCAmelCase__="<sep>" ,  lowerCAmelCase__="<pad>" ,  lowerCAmelCase__="<cls>" ,  lowerCAmelCase__="<mask>" ,  lowerCAmelCase__=["<eop>", "<eod>"] ,  lowerCAmelCase__ = None ,  **lowerCAmelCase__ ,  )	->   None:

							'''simple docstring'''


							__lowercase					     =				AddedToken(lowerCAmelCase__ ,  lstrip=lowerCAmelCase__ ,  rstrip=lowerCAmelCase__		) if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		) else mask_token

							__lowercase					     =				{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=lowerCAmelCase__ ,  remove_space=lowerCAmelCase__ ,  keep_accents=lowerCAmelCase__ ,  bos_token=lowerCAmelCase__ ,  eos_token=lowerCAmelCase__ ,  unk_token=lowerCAmelCase__ ,  sep_token=lowerCAmelCase__ ,  pad_token=lowerCAmelCase__ ,  cls_token=lowerCAmelCase__ ,  mask_token=lowerCAmelCase__ ,  additional_special_tokens=lowerCAmelCase__ ,  sp_model_kwargs=self.sp_model_kwargs ,  **lowerCAmelCase__ ,  )

							__lowercase					     =				3

							__lowercase					     =				do_lower_case
							__lowercase					     =				remove_space
							__lowercase					     =				keep_accents
							__lowercase					     =				vocab_file

							__lowercase					     =				spm.SentencePieceProcessor(**self.sp_model_kwargs		)
							self.sp_model.Load(lowerCAmelCase__		)





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							return len(self.sp_model		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				{self.convert_ids_to_tokens(lowerCAmelCase__		): i for i in range(self.vocab_size		)}
							vocab.update(self.added_tokens_encoder		)
							return vocab





			def __getstate__(  self		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				self.__dict__.copy()
							__lowercase					     =				None
							return state





			def __setstate__(  self ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				d

							# for backward compatibility
							if not hasattr(self ,  '''sp_model_kwargs'''		):
											__lowercase					     =				{}

							__lowercase					     =				spm.SentencePieceProcessor(**self.sp_model_kwargs		)
							self.sp_model.Load(self.vocab_file		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Optional[Any]:

							'''simple docstring'''


							if self.remove_space:
											__lowercase					     =				''' '''.join(inputs.strip().split()		)
							else:
											__lowercase					     =				inputs
							__lowercase					     =				outputs.replace('''``''' ,  '''"'''		).replace('''\'\'''' ,  '''"'''		)

							if not self.keep_accents:
											__lowercase					     =				unicodedata.normalize('''NFKD''' ,  lowerCAmelCase__		)
											__lowercase					     =				''''''.join([c for c in outputs if not unicodedata.combining(lowerCAmelCase__		)]		)
							if self.do_lower_case:
											__lowercase					     =				outputs.lower()

							return outputs





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				self.preprocess_text(lowerCAmelCase__		)
							__lowercase					     =				self.sp_model.encode(lowerCAmelCase__ ,  out_type=lowerCAmelCase__		)
							__lowercase					     =				[]
							for piece in pieces:
											if len(lowerCAmelCase__		) > 1 and piece[-1] == str(''','''		) and piece[-2].isdigit():
															__lowercase					     =				self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase__ ,  ''''''		)		)
															if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																			if len(cur_pieces[0]		) == 1:
																							__lowercase					     =				cur_pieces[1:]
																			else:
																							__lowercase					     =				cur_pieces[0][1:]
															cur_pieces.append(piece[-1]		)
															new_pieces.extend(lowerCAmelCase__		)
											else:
															new_pieces.append(lowerCAmelCase__		)

							return new_pieces





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   int:

							'''simple docstring'''


							return self.sp_model.PieceToId(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Any:

							'''simple docstring'''


							return self.sp_model.IdToPiece(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				''''''.join(lowerCAmelCase__		).replace(lowerCAmelCase__ ,  ''' '''		).strip()
							return out_string





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = False ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   str:

							'''simple docstring'''


							__lowercase					     =				kwargs.pop('''use_source_tokenizer''' ,  lowerCAmelCase__		)

							__lowercase					     =				self.convert_ids_to_tokens(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)

							# To avoid mixing byte-level and unicode for byte-level BPT
							# we need to build string separately for added tokens and byte-level tokens
							# cf. https://github.com/huggingface/transformers/issues/1133
							__lowercase					     =				[]
							__lowercase					     =				[]
							for token in filtered_tokens:
											if skip_special_tokens and token in self.all_special_ids:
															continue
											if token in self.added_tokens_encoder:
															if current_sub_text:
																			sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__		)		)
																			__lowercase					     =				[]
															sub_texts.append(lowerCAmelCase__		)
											else:
															current_sub_text.append(lowerCAmelCase__		)
							if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__		)		)

							# Mimic the behavior of the Rust tokenizer:
							# By default, there are no spaces between special tokens
							__lowercase					     =				''''''.join(lowerCAmelCase__		)

							__lowercase					     =				(
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
											__lowercase					     =				self.clean_up_tokenization(lowerCAmelCase__		)
											return clean_text
							else:
											return text





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   List[int]:

							'''simple docstring'''


							__lowercase					     =				[self.sep_token_id]
							__lowercase					     =				[self.cls_token_id]
							if token_ids_a is None:
											return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = False		)	->   List[int]:

							'''simple docstring'''


							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowerCAmelCase__ ,  token_ids_a=lowerCAmelCase__ ,  already_has_special_tokens=lowerCAmelCase__		)

							if token_ids_a is not None:
											return ([0] * len(lowerCAmelCase__		)) + [1] + ([0] * len(lowerCAmelCase__		)) + [1, 1]
							return ([0] * len(lowerCAmelCase__		)) + [1, 1]





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   List[int]:

							'''simple docstring'''


							__lowercase					     =				[self.sep_token_id]
							__lowercase					     =				[2]

							if token_ids_a is None:
											return len(token_ids_a + sep		) * [0] + cls_segment_id
							return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   Tuple[str]:

							'''simple docstring'''


							if not os.path.isdir(lowerCAmelCase__		):
											logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
											return
							__lowercase					     =				os.path.join(
							    lowerCAmelCase__ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

							if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCAmelCase__		) and os.path.isfile(self.vocab_file		):
											copyfile(self.vocab_file ,  lowerCAmelCase__		)
							elif not os.path.isfile(self.vocab_file		):
											with open(lowerCAmelCase__ ,  '''wb'''		) as fi:
															__lowercase					     =				self.sp_model.serialized_model_proto()
															fi.write(lowerCAmelCase__		)

							return (out_vocab_file,)
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
		import datasets




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			@slow
			@require_torch
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' ,  '''prajjwal1/bert-tiny'''		)
							__lowercase					     =				BertTokenizer.from_pretrained('''bert-base-uncased'''		)

							__lowercase					     =				bertabert.config.encoder.vocab_size
							__lowercase					     =				tokenizer.sep_token_id
							__lowercase					     =				tokenizer.cls_token_id
							__lowercase					     =				1_28

							__lowercase					     =				datasets.load_dataset('''cnn_dailymail''' ,  '''3.0.0''' ,  split='''train[:1%]'''		)
							__lowercase					     =				datasets.load_dataset('''cnn_dailymail''' ,  '''3.0.0''' ,  split='''validation[:1%]'''		)

							__lowercase					     =				train_dataset.select(range(32		)		)
							__lowercase					     =				val_dataset.select(range(16		)		)

							__lowercase					     =				4

							def _map_to_encoder_decoder_inputs(lowerCAmelCase__		):
											# Tokenizer will automatically set [BOS] <text> [EOS]
											__lowercase					     =				tokenizer(batch['''article'''] ,  padding='''max_length''' ,  truncation=lowerCAmelCase__ ,  max_length=5_12		)
											__lowercase					     =				tokenizer(batch['''highlights'''] ,  padding='''max_length''' ,  truncation=lowerCAmelCase__ ,  max_length=1_28		)
											__lowercase					     =				inputs.input_ids
											__lowercase					     =				inputs.attention_mask

											__lowercase					     =				outputs.input_ids
											__lowercase					     =				outputs.input_ids.copy()
											__lowercase					     =				[
											    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
											]
											__lowercase					     =				outputs.attention_mask

											assert all(len(lowerCAmelCase__		) == 5_12 for x in inputs.input_ids		)
											assert all(len(lowerCAmelCase__		) == 1_28 for x in outputs.input_ids		)

											return batch

							def _compute_metrics(lowerCAmelCase__		):
											__lowercase					     =				pred.label_ids
											__lowercase					     =				pred.predictions

											# all unnecessary tokens are removed
											__lowercase					     =				tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)
											__lowercase					     =				tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)

											__lowercase					     =				sum([int(pred_str[i] == label_str[i]		) for i in range(len(lowerCAmelCase__		)		)]		) / len(lowerCAmelCase__		)

											return {"accuracy": accuracy}

							# map train dataset
							__lowercase					     =				train_dataset.map(
							    _map_to_encoder_decoder_inputs ,  batched=lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  remove_columns=['''article''', '''highlights'''] ,  )
							train_dataset.set_format(
							    type='''torch''' ,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] ,  )

							# same for validation dataset
							__lowercase					     =				val_dataset.map(
							    _map_to_encoder_decoder_inputs ,  batched=lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  remove_columns=['''article''', '''highlights'''] ,  )
							val_dataset.set_format(
							    type='''torch''' ,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] ,  )

							__lowercase					     =				self.get_auto_remove_tmp_dir()

							__lowercase					     =				SeqaSeqTrainingArguments(
							    output_dir=lowerCAmelCase__ ,  per_device_train_batch_size=lowerCAmelCase__ ,  per_device_eval_batch_size=lowerCAmelCase__ ,  predict_with_generate=lowerCAmelCase__ ,  evaluation_strategy='''steps''' ,  do_train=lowerCAmelCase__ ,  do_eval=lowerCAmelCase__ ,  warmup_steps=0 ,  eval_steps=2 ,  logging_steps=2 ,  )

							# instantiate trainer
							__lowercase					     =				SeqaSeqTrainer(
							    model=lowerCAmelCase__ ,  args=lowerCAmelCase__ ,  compute_metrics=_compute_metrics ,  train_dataset=lowerCAmelCase__ ,  eval_dataset=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__ ,  )

							# start training
							trainer.train()